Calling All Contact Centers: Analyze and Optimize with Big Data

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Contact centers are always looking for any extra leverage to provide the best service. Customer satisfaction is often regarded as being one of the key metrics for measuring success of the center. How exactly does a contact center understand their customer satisfaction, though? We took a look at some of the best add-ons your contact center needs in 2017, but some might be left wondering just how their contact center can benefit from these new tools.

Big data has been a bit of a buzzword floating around for a while now, but if there’s any industry that collects big data, it has to be contact centers. It’s this massive volume of data that contact centers use to understand how successful their center is. Converting the mass data into actionable insights enables contact centers to recognize strong points and, more importantly, weak points.

By leveraging the mass data your center generates, it is even possible to establish predictive analytics. What better way to keep your callers and clients happy than to predict their needs and act before they even ask?

Tracking Your Contact Center Performance

The simple concept of call center analytics has expanded over the years. As we’ve seen call centers grow into contact centers, new channels have opened up with new technologies to better equip agents with the resources they need. However, this also means that the amount of information that comes into any single system has also risen. The important part is understanding how to track and analyze this information with the right set of tools.

For example, as more centers began to adopt social media platforms, a whole new analytical approach was developed to handle social analytics. Overall, however, call center or contact center analytics can be understood as being the necessary tools to collect, organize, and utilize data to keep their contact center preforming optimally. Chatbots themselves can benefit immensely from understanding how users interact with them.

But I Still Don’t Understand, “Big Data?”

A fairly general concept, big data simply refers to the collection of massive amounts of information. Have you ever called in for support and were greeted with: “Your call may be recorded for training and quality purposes?” At this point, it’s probably synonymous with screaming, “Agent,” at the IVR or hitting “I Accept” on those internet disclaimers and agreements. What many may not realize, though, is that contact centers truly are utilizing these recordings for quality purposes.

The term “big data” is a fairly new one, but the idea of collecting and storing information is age-old. Just in recent years, we’ve been able to benefit from the computer’s amazing power of quickly collecting and even analyzing data for us. Artificial Intelligence will be an incredibly powerful tool for contact centers in many ways, but the biggest benefits definitely come with analytics. Overall, big data is important for any business to:

Recognize areas of failure and determine the root cause of the specific failure.

Recognize areas of success and understand how to apply that formula to other aspects of your business.

Detect and prevent fraudulent behavior and access before it impacts your contact center.

Now we’re really diving into science fiction with speech analytics. With the invention of natural language processing technologies, our analytical tools are capable of understanding more than just raw excel spreadsheets of data. In fact, as I mentioned before, contact centers have what seems like an unlimited number of recordings when it comes to phone support.

Now, natural language processing isn’t incredibly new — it’s what powers the dreaded IVR system. However, with new developments in both NLP technologies — and also artificial intelligence and machine learning that can even benefit your omnichannel strategy — these systems can interpret more than just a single word response. In fact, you may have noticed that modern (or smart) IVR systems have been taking over the frustrating one-word response machines.

How Can My Contact Center Leverage This?

These systems are capable of understanding sentences and asking more generalized questions. Speech analytics can be processed through modern NLP technologies that understand both what you say, in complex sentences, and even how you say it. This means that the specific words or tone you use (that’s right, angry callers — they know) allows the system to gauge a customer’s satisfaction.

Based on this, a speech analytics system can be used to better direct customers to the help they need, or even assist agents with specific information triggered by specific phrases or words. When a customer says, “I want to speak to a manager,” the speech analytics tool can provide the agent with the proper protocol to handle this request. Some systems are even scary good, and can attempt to predict more specific information like a caller’s age, which:

Provides a smart and modern IVR experience with more capable speech analytics.

Helps analyze callers in real-time to assist agents with appropriate responses, or even security measures.

Predictive Analytics

I touched upon this previously, but there is no better service than anticipating exactly what your customers and clients will need before they even pick up the phone. Now, we’re not talking about mind-reading of course. But, as we’ve seen with the recent AI developments, we really aren’t too far off of making science fiction a reality. Predictive analytics are very real, and here to help improve your contact center.

These tools utilize the in-depth information your center collects on each and every interaction. Specifically, these solutions highlight past performance in a number of areas including: customer request volume, the service level of each request, handling time for requests, and customer satisfaction. Combine your predictive analytics with speech analytics, and you now have a system that can acutely predict a customer’s reaction or request in a call, in real-time, to assist the agent with the most appropriate response.

How Can My Contact Center Leverage This?

By understanding why customers call, what level of support they require, how quickly the issue was resolved, and the customer’s satisfaction with that resolution, your contact center can learn how to best handle requests later. Predictive analytics are generally utilized for broader scenarios, such as recognizing peak call times. For example, will your contact center need more hires to handle the Christmas flood of support requests for a new product, or will this new product increase specific support requests during the week?

Predicting your contact center’s trends and learning from the past will help your center prepare for the future. As we noted in big data, predictive analytics can even help prevent fraud. This is because speech and predictive analytics combined can anticipate when a caller is lying and attempt to deceive the agent. Predictive analytics helps a business improve their customer experience by:

Anticipating customer needs during phone calls and support requests.

Utilizing predictive analysis of customer trends to establish and act on future strategies.

Text and Social Analytics

Contact centers evolved out of the need to adopt new communication channels. Keeping up with the times means keeping up with how customers prefer to interact. Email support has been around for awhile, and text-based support is rapidly taking over. But even more importantly, so are chat bots. Facebook recently made some interesting changes to chatbots by adding in a new “persistent menu” option for customers to navigate. This is undoubtedly a response to the usage of chatbots, or lack thereof.

In fact, Facebook even went ahead and released some of their own new analytical tools to measure chatbot performance. So, as these new channels open up, contact centers need to equip themselves with the tools to best handle that new flow of information. Beyond just Facebook’s chatbots, text analytic tools exist to help a company monitor all and every text communication.

How Can My Contact Center Leverage This?

These analytical tools can scan both incoming and outgoing messages. This means your agents will have an extra assistant sitting over their shoulders analyzing their messages. This can help improve agent response times by not only guiding them towards a proper response, but also tailoring responses for customer satisfaction.

On the flip side, text analytics can take a look at the messages customer and clients are sending your agents, and just like predictive analytics, this information can be used to help formulate appropriate responses or anticipate a customer’s needs. Social analytics, of course, refer to text-based analytical tools that focus directly on your social media channels to help your business maintain a proper social media presence.

Maintain consistency and professionalism in all channels even your text based support.

Stay on top of your social media presence by measuring engagement and satisfaction.

IVR or Self-Service Analytics

Self-service options have been growing rapidly as many customers have become increasingly frustrated with old-fashioned IVR systems. At this point, navigating through a menu with a one word response simply isn’t intuitive and can prove frustrating for most callers. It does help that IVR systems are quickly becoming more capable and even smarter in some cases.

At the same time, however, new self-service methods are being implemented, such as in-app support from HelpShift. Customers won’t even have to leave your business’ mobile app to submit a service request — they may even sometimes be able to handle the request themselves. This is crucial because it cuts down on agent time, allowing your center to handle the issues that truly require a live-human solution.

How Can My Contact Center Leverage This?

These self-service systems will only be as effective as they are useful — meaning if your business employs a menu option in your app or chatbot, and your system is bogged down with useless options, customers will just end up being even more frustrated and confused. This, of course, is where the analytical tools come in. In fact, self-service analytics requires even less interactions on the part of your agents or contact center managers than other solutions.

Optimizing your menus based on customer usage patterns will help create an effective system that provides the important information customers need in an easy to navigate menu. This helps reduce the redundancy of your agents, constantly handling the simple five-second requests. Freeing up your agents means that they can pay more attention to the issues that require increased support.

Omnichannel and Digital Experience Analytics

With omnichannel becoming an incredibly popular strategy for the modern contact center, it is important to measure the success of these interconnected channels. Omnichannel, and cross-channel, analytics will measure the adoption rate of the different channels of communication that your contact center provides.

A retail business, for example, can measure store POS systems, any online transactions, social media promotions, and even call center records to help formulate a deep understanding of the exact journey a customer took.

How Can My Contact Center Leverage This?

Omnichannel analyzation is about understanding the path your customer took to jump from channel to channel, and how to make that path easier. After all, the omnichannel strategy is all about connecting different tunnels, or islands, into one cohesive experience for your customers.

So if your business recognizes that customers are often browsing the website, but are lost while in the process of ordering, this information can be used to improve that specific path. Omnichannel analytics will be an incredibly important tool for your business to measure the success and ease of their digital experience. After all, as we all know, if something is too complicated, then it simply won’t be used.

Optimize the omnichannel experience your business provides, guiding customers along the right path.

Recognize where, when, how, and why a customer jumps from one point to the next to prevent abandoned interactions.

Analytics Are The Best Way to Improve and Optimize

Coming full circle to how I kicked off this post — customer satisfaction is absolutely key. Customers and clients have the power to vote with their wallet. If they leave a support request unsatisfied, that’s one of the easiest ways to find your business on a list of companies to avoid.

Some of the most common complaints consumers have about contact centers are the ridiculous hold times, a disappointing resolution, or the frustrating experience of being juggled between agents with no end in sight. But just as contact centers were so quick to adopt predictive and power dialers, they should also be willing to throw in analytics to the mix.

Build The Best Customer Experience

Just as it can be difficult to please customers who call into your contact center, developing a useful digital experience is also a fine line of balancing. You don’t want to provide too much information to a customer and overwhelm them, but you also don’t want customers ditching the website and picking up the phone because it seems easier. Because analytics affect many different aspects of your contact center and communication channels, they provide the best metrics to measure your center’s success by.

Every single contact center is capable of collecting massive sets of data, but the real differentiator is how exactly your center will apply this data. Utilizing the information your analytics systems gather, your center will have a stronger understanding of how to build the best experience possible, from start to finish. Analyzing call center metrics allows your center to easily and quickly recognize any flaws or roadblocks in the system, and vastly improve the level of service your contact center is capable of providing.